Summary of Work: The purpose of this project is to develop methodology for analyzing molecular population genetics data. Work has focused on the use of nonparametric methods for localizing susceptibility loci for complex diseases in humans. A positive test of association following a positive test of linkage provides additional support that a marker is physically near a disease locus. We have generalized the transmission disequilibrium test (TDT)to a test for association in the presence of linkage for families with two affected sibs, and have also shown how families with different numbers of affected sibs can be combined into a single test. This test does not discard data and is therefore more powerful than an earlier method that uses the TDT but only considers a single affected child from each family. A genome screen using sib-pair analysis to test for linkage can identify candidate regions that may contain susceptibility loci. One strategy to determine the best place to start looking in a candidate region is to use a much finer screen in the region using TDT analysis. Using permutation methods we developed a way for dealing with the multiple testing problem that is always as least as powerful as the traditional Bonferroni approach and if the markers in the screen are associated, then it is more powerful. Association in admixed populations can persist over much larger regions than in general populations and therefore the TDT can have more power in these special populations. Using earlier results we have developed a way to compare the power of the TDT fortwo markers that are the same genetic distance from a disease locus. This result allows us to compare the power of the TDT for a microsatellite with any of its collapsed 2-allele markers. This result may improve the performance of a commercial battery of microsatellite markers when doing a genome scan in an admixed population. For some genetic diseases, especially bipolar disease, a major problem is the large number of different diagnoses. To deal with this issue we developed a permutation test that uses marker data for assessing an affection status model. This test provides an objective criteria for choosing between different models based on the observed marker data.